3.5
Kb
min
4sUDRB-seq: measuring genomewide
transcriptional elongation rates and initiation
frequencies within cells
Fuchs et al.
Fuchs et al. Genome Biology 2014, 15:R69
http://genomebiology.com/2014/15/5/R69
Fuchs et al. Genome Biology 2014, 15:R69
http://genomebiology.com/2014/15/5/R69
METHOD
Open Access
4sUDRB-seq: measuring genomewide
transcriptional elongation rates and initiation
frequencies within cells
Gilad Fuchs1*†, Yoav Voichek2†, Sima Benjamin3, Shlomit Gilad3, Ido Amit4 and Moshe Oren1*
Abstract
Although transcriptional elongation by RNA polymerase II is coupled with many RNA-related processes, genomewide
elongation rates remain unknown. We describe a method, called 4sUDRB-seq, based on reversible inhibition of
transcription elongation coupled with tagging newly transcribed RNA with 4-thiouridine and high throughput
sequencing to measure simultaneously with high confidence genome-wide transcription elongation rates in cells.
We find that most genes are transcribed at about 3.5 Kb/min, with elongation rates varying between 2 Kb/min and
6 Kb/min. 4sUDRB-seq can facilitate genomewide exploration of the involvement of specific elongation factors in
transcription and the contribution of deregulated transcription elongation to various pathologies.
Background
Gene transcription is a multistep process consisting of
recruitment of RNA polymerase II (Pol II) to promoters,
transcription initiation, elongation, and termination. In
addition to producing RNA polymers based on the DNA
template, the Pol II holoenzyme also regulates numerous
RNA processing events, including 5′ cap formation, splicing, polyadenylation, and RNA transport [1-7].
While historically most studies focused on understanding how promoter binding and transcription initiation are regulated, recent studies have shown that
additional stages of the transcription process are also
tightly regulated and are critical for gene activation.
These studies have demonstrated that binding of Pol II
to gene promoters is not sufficient for productive transcription. Instead, in the majority of genes Pol II is partly
‘paused’ 20 to 60 nt from the transcription start site
(TSS), and several regulated steps are needed in order
for Pol II to depart from the TSS and transcribe the rest
of the gene [8-15].
The rate of Pol II movement through gene bodies has
also been linked to various aspects of co-transcriptional
RNA processing. For example, changes in transcription
* Correspondence: [email protected]; [email protected]
†
Equal contributors
1
Department of Molecular Cell Biology, Weizmann Institute of Science,
Rehovot 76100, Israel
Full list of author information is available at the end of the article
elongation rates can affect the outcome of the splicing
machinery [7,16,17], and slow elongation by Pol II was
shown to enhance the inclusion of specific weak exons
[7]; in support of this notion, Pol II was found to accumulate at exons [18-20]. Likewise, the rate of elongation
by Pol II has been linked to regulation of alternative
polyadenylation [21]; indeed, Pol II accumulates at polyadenylation sites [9].
The importance of steps subsequent to transcriptional
initiation is also underscored by the impact of misregulation of these steps on cellular and organismal viability.
For example, many of the MLL gene translocation partners, thought to drive aggressive acute leukemia, are part
of the super elongation complex (SEC). Leukemiaassociated MLL fusion proteins relocalize the SEC to
MLL target genes, bypassing normal transcriptional control and causing aberrant expression of those genes [22].
Similarly, excessive c-Myc was suggested to augment
gene expression by increasing the release of paused Pol
II and thus facilitating active elongation, thereby alleviating rate-limiting constraints on tumor cell growth and
proliferation [23]. Furthermore, viruses utilize or modify
transcription elongation-related processes to their benefit. For example, the influenza A NS1 protein comprises
a histone-like sequence that can target the PAF1 elongation complex, enabling selective modulation of host cell
gene expression and contributing to suppression of the
© 2014 Fuchs et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.
Fuchs et al. Genome Biology 2014, 15:R69
http://genomebiology.com/2014/15/5/R69
antiviral response [24]. In addition, during active HIV-1
infection, viral transactivator of transcription (Tat) recruits the SEC to the HIV-1 long terminal repeat (LTR)
to activate expression of the provirus in host cells
[25-27]. Thus, better elucidation of the regulation of
transcriptional elongation could contribute to the understanding of molecular mechanisms of disease, and even
suggest novel therapeutic approaches.
Despite the documented links between elongation
rates and numerous RNA-related functions, the actual
elongation rates within cells remain under debate, with
reported values ranging from 1 kilobase per minute
(Kb/min) to 6 Kb/min [28]. In most cases, elongation
rates of only a few genes at a time were measured; one
study, utilizing Global Run-On sequencing (GRO-seq),
was able to determine the rates for approximately 166
long genes upregulated in response to short treatment
with physiological, non-toxic inducers [29]. Since the
elongation rates of individual genes can be altered in a
stimulus-dependent manner [29], it is important to
measure the elongation rates of non-stimulated genes in
order to derive more general understanding of the relationship between basal transcription elongation rates
and steady state RNA processing and gene expression.
Moreover, assessment of a large number of genes may
provide more definitive information on the factors that
affect transcription elongation rates. Here we describe a
method that enables to easily measure simultaneously,
in a single experiment, the steady state elongation rates
of thousands of genes within live cells.
Results and discussion
Adaptation of the DRB assay for measuring elongation
rates in short time scales by RNA sequencing
Previously, Singh and Padgett [30] employed 5,6-dichlorobenzimidazole 1-β-d-ribofuranoside (DRB), which reversibly blocks transcription in vivo, combined with
quantitative reverse transcriptase-PCR (qRT-PCR), to assess the elongation rates of several long human genes.
We sought to adapt this method for simultaneous determination of the elongation rates of all DRB-sensitive
genes. Since the median length of a human gene is approximately 24 Kb and typical elongation rates are estimated at a few Kb/min, measurements were performed
4 and 8 min after DRB removal. Pre-mRNA levels were
quantified by qRT-PCR, employing primers specific for
intronic sequences. Analysis of pre-mRNA levels in
HeLa cells incubated with DRB for 3 h revealed that
transcription of a proximal region of the OPA1 gene, located 2 Kb downstream to the TSS, was recovered
already within 4 min of DRB removal (Figure 1A). In
contrast, full transcriptional recovery of a distal region
of the same gene, located 10 Kb downstream to the TSS,
occurred only 8 min after drug release. A similar trend
Page 2 of 10
was observed for TTC17 (Figure 1A) and several other
genes (data not shown). Hence, the DRB protocol can
capture the progress of Pol II also in average-sized
genes.
In order to obtain genomewide information on elongation rates, this protocol had to be adapted to RNA-seq.
Nascent RNA constitutes only a very small fraction of
the total RNA within cells; therefore, it is necessary to
enrich it prior to sequencing. In principle, such enrichment is achievable by GRO-seq, which is becoming increasingly popular [29,31-33]. However, GRO-seq
requires prior isolation of nuclei, which takes a relatively
long time; this might introduce biases in short time scale
measurements. We therefore added to the protocol a
step of labeling nascent RNA in vivo with 4-thiouridine
(4sU), which can be incorporated into intact cells
[34,35], followed by specific purification of 4sUcontaining RNA.
To test whether short 4sU labeling can indeed enrich
for nascent RNA, HeLa cells were treated with 4sU for 8
min. 4sU-tagged RNA was biotinylated in vitro, purified
on streptavidin beads and subjected to qRT-PCR analysis. Pre-mRNA/mature RNA ratios were calculated for
OPA1 and TTC17 both in the 4sU-enriched fraction and
in the total (non-enriched) RNA. Similar to a previous
report [35], the short 4sU labeling yielded an approximately 20- to 35-fold enrichment of nascent RNA of
these genes relative to the total RNA population
(Figure 1B).
We next set out to determine the rate of movement of
RNA Pol II on individual genes. To that end, we monitored the changes in nascent RNA abundance throughout those genes at different time points after resumption
of RNA synthesis following DRB removal. HeLa cells
were treated with DRB for 3 hours and harvested either
4 or 8 min following DRB removal. In both cases, the
cells were pulsed with 4sU for the last 8 min before being harvested (Additional file 1). Cells were harvested in
QIAzol lysis buffer directly on the culture dish. Next,
4sU-tagged nascent RNA from each sample was biotinylated and collected on streptavidin beads. This experimental procedure was performed separately in two
biological repeats for the 4 min samples and four biological repeats for the 8 min samples. Finally, the RNA
was subjected to deep sequencing (see Materials and
Methods). The entire protocol, schematically illustrated
in Figure 1C, was termed 4sUDRB-seq.
Genomewide analysis of transcription elongation rates by
4sUDRB-seq
Given the high correlation between biological repeats
(Additional file 2), we averaged the 4sUDRB-seq signals
of the 4 and 8 min samples from the biological repeats
over all genes longer than 20 Kb. We observed a distinct
Fuchs et al. Genome Biology 2014, 15:R69
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relative RNA levels
3 proximal +2kb
distal +10kb
2
1
0
pre-mRNA TTC17/18S
relative RNA levels
pre-mRNA OPA1/18S
proximal +1kb
2 distal +10kb
1
0
NT DRB 4 min 8 min
NT DRB 4 min 8 min
B
pre-mRNA/mRNA (relative)
A
Page 3 of 10
40 TTC17
30
OPA1
20
10
0
CON
4sU
C
NT
DRB
biotinlyation
4 min
Streptavidin
beads
RNA-Seq
8 min
Figure 1 Adaptation of the DRB assay for measuring elongation rates in short time scales by RNA sequencing. (A) Analysis of OPA1 and
TTC17 pre-RNA in HeLa cells, immediately after 3 h DRB treatment (DRB) and at the indicated time points after DRB removal. NT = non-treated.
Pre-mRNA levels were determined by qRT-PCR, employing intronic primers. All values were normalized to 18S RNA in the same sample. Levels in
non treated cells were set as 1. Bars indicate averages of three independent experiments; error bars represent standard deviation. (B) Relative
pre-mRNA/mRNA ratios in total RNA (CON) and 4sU-enriched RNA (4sU). HeLa cells were labeled for 8 min with 4sU (1mM). RNA was isolated,
biotinylated, and enriched on streptavidin beads. qRT-PCR analysis of OPA1 and TTC17 pre-mRNA and mRNA was performed on the enriched
RNA, as well as on total (non-enriched) RNA. The pre-mRNA/mRNA ratio for each gene in the CON sample was arbitrarily defined as 1.0. (C)
Scheme of the timing of DRB and 4sU treatments in the different conditions. NT = non-treated, DRB = 3 h of DRB only, 4 min = 3 h of DRB and
harvesting 4 min after DRB removal, 8 min = 3 h of DRB and harvesting 8 min after DRB removal. Red squares = 4sU, orange stars = biotin.
Following the indicated treatments, isolated 4sU-labeled RNA was biotinylated and purified with magnetic streptavidin beads as described in
[42] and subjected to high throughput sequencing.
wave of transcription progression (Figure 2A). As expected, the nascent RNA reads in the 8 min sample extended much beyond the 4 min sample, relative to the
TSS (marked as 0). Of note, the average distance traversed by Pol II between 4 and 8 minutes appears substantially longer than that attained in the first 4 min; this
might imply either a slower elongation rate in the region
immediately adjacent to the TSS followed by subsequent
acceleration, or a short delay in resumption of transcription following DRB removal. A similar pattern was revealed by examination of single gene reads, showing a
clear advance of nascent RNA synthesis between the 4
min and 8 min time points (Figure 2B, C).
The density of reads decreases gradually towards the
advancing front of the transcription wave (Figure 2B, C).
Hence, the exact border of the advancing transcription
wave is not sharp and is hard to define. Furthermore,
DRB does not enforce a complete transcription arrest, as
evident by the presence of scattered low abundance
reads throughout gene bodies (Figure 2B, C). Therefore,
we developed an algorithm that determines the front
border of the elongation wave for each gene at a given
time point, taking into consideration the above observations. To avoid biases due to sequence properties, the
pre-DRB release pattern was used for correction. Only
genes with a primary transcript longer than 25 Kb were
considered.
To determine the front end of the elongation wave for
each transcript at 4 and 8 min after DRB release, we performed two sequential steps. First, we attempted to define the approximate position of the front end (‘rough’
estimate) by identifying the 5’-most point within the
gene where the number of normalized reads is not any
higher than at the 0 time point (Figure 3A). In the second step, we refined this estimate using the shape of the
pattern. Basically, the algorithm looks at the derivative
of the pattern while correcting for the background. As
the abundance of reads decreases towards the front
boundary of the elongation wave, the pattern derivative
should become negative; however, at the exact location
of the boundary it should become 0, as it is not affected
anymore by transcription initiation due to DRB release
(Figure 3A). Hence, the first position upstream to the region identified by the ‘rough’ estimate where the derivative became 0 was defined as the wave end point.
In some cases, the algorithm identified a clear front
boundary for a particular gene in some of the experiments, but failed to do so in one or more of the other
Fuchs et al. Genome Biology 2014, 15:R69
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Page 4 of 10
log2(signal)
A
0.15
4 min average signal
8 min average signal
0.2
0.1
0.1
0.05
0
0
0
5k
10k 15k 20k
25k
30k 35k
0
40k
5k
10k
15k
20k
25k
30k
35k
40k
Position after TSS
Position after TSS
B
C
PFDN1
RBBP6
4sUDRB-seq signal
NT
0 min
4 min
4 min
8 min
8 min
24550K
24560K
24570K
24580K
Chr XVI
139640K
139660K
139680K
Chr V
Figure 2 In vivo elongation rate analysis of thousands of genes simultaneously. (A) Averaged distribution of 4sUDRB-seq reads over the
different biological repeats in all genes longer than 20 Kb in RNA harvested 4 or 8 min after DRB removal. To minimize distortion of the data by
residual mature mRNA, only reads within introns were considered and averaged. (B, C). 4sUDRB-seq reads distribution along the RBBP6 and PFDN1
genes. Arrow marks the direction of transcription.
experiments (data not shown). Consequently, we calculated elongation rates only for genes that yielded distinct
boundaries in at least two independent repeats of a given
time point. To assess the reliability of our measurements, the confidence level of the elongation rate for
each gene was calculated (Materials and Methods). As
seen in Additional file 3, for most of the genes the confidence level was better than ± 0.5 Kb/min. For subsequent analysis, only genes with confidence levels better
than ± 0.5 Kb/min were considered (Additional file 3). In
addition, the estimated delay time, namely the time it
takes transcription to reinitiate effectively following DRB
removal, was also calculated; this was defined as the
time interval missing in order for the distance between
the TSS and the 4 min boundary to be equal to the distance between the 4 and 8 min boundaries, given the
calculated elongation rate. This calculation is based on the
assumption that the elongation rate is uniform throughout
the gene. However, it remains formally possible that the
rate is slower at the beginning of the gene, in which case
the calculated delay times may represent overestimates.
Overall, we could measure with high confidence the
elongation rates of 1,577 genes. The full list of elongation rates generated by this analysis is presented in
Additional file 4. Examples of elongation rate measurements for several representative genes are shown in
Figure 3B. As also seen in Figure 3C, while most genes
are transcribed at a rate of approximately 3.5 Kb/min,
actual transcription elongation rates vary between
2 Kb/min and 6 Kb/min. Overall, these rates are in the
same range as those determined previously for a small
number of specific genes [28,30].
Fuchs et al. Genome Biology 2014, 15:R69
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A
Page 5 of 10
NDUFV2
“Rough” estimate
Normelize signal
4sUDRB-seq signal
20
T=0 min
Normalize
to background
signal
T=4 min
9100K
9110K
9120K
9130K
30
20
10
0
0
4
0
30
4
0
0
4
8
0
4
D
8 min / 0 min
signal
Derivative
4
8
8 min-R1
8 min-R2
SPG7
8 min-R3
10
8 min-R4
5
0
8
0
Time (min)
4
8
Time (min)
H3K36me3
H3K79me2
p = 0.57
mean:
3.6 Kb/min
5
log2(Signal)
# genes
4 min-R2
15
10
200
160
4 min-R1
0
Time (min)
20
0
C
10 20 30
basepairs (Kb)
TYW1
0
STAG1
Time (min)
0
10
8
Boundary (Kb)
20
peak
20
Time (min)
CSDE1
Plateau
0
10 20 30
basepairs (Kb)
30
10
0
Boundary (Kb)
Boundary (Kb)
40
10
20
30
basepairs (Kb)
differentiate
20
8
0
0
0
AEBP2
Time (min)
4
4
9140K
Boundary (Kb)
NDUFV2
Boundary (Kb)
Boundary (Kb)
30
8min
8
8
Chr XVIII
B
4min
12
0
T=8 min
0min
16
120
80
40
6
4.5
4
5
3.5
4
3
0
0
3
1
2
3
4
5
6
7
Elongation rate [Kb/min]
2.5
High
Low
Elongation rate
High
Low
Elongation rate
Figure 3 In vivo genomewide measurement of transcription elongation rates. (A) Schematic representation of the algorithm used to infer
elongation rates, exemplified for the NDUFV2 gene. Left panel: 4sUDRB-seq data for 0/4/8 min after DRB release (similar to Figure 2B). Each signal
was then corrected for the inferred background signal (right top panel), identifying for the 4 and 8 min samples the first position downstream
to the TSS where the corrected signal is similar to that of the 0 time point; this location is designated by a vertical dashed line. To refine the
boundary identification, we evaluated the background-corrected signals of the 4 and 8 min time points divided by the 0 time point signals and
their derivatives. Refinement of the boundary position was then performed by monitoring the decreasing signal area in the vicinity of the rough
boundary estimate, and determining the location between the closest peak and the point where the signal plateaus. (B) Linear fitting was
performed on the averaged 4 and 8 min elongation boundaries as a function of time for the indicated genes. Elongation rates were defined by
extracting the slope value of the linear fit (V). Confidence interval is indicated for each gene. (C) Distribution of measured elongation rates of all
informative DRB-sensitive genes. (D) Box-whisker plot of log2 transformed H3K36me3 and H3K79me2 levels in genes that overlap with each
modification in the 25% of genes with the highest calculated elongation rate and the 25% of genes with the lowest elongation rate. H3K79me2
is significantly higher in fast elongating genes (t-test, P = 1e-7). All data were adapted from ENCODE.
Fuchs et al. Genome Biology 2014, 15:R69
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start moving into the gene body (Figure 4A) [40]. Thus, a
greater number of such events occurring within a defined
time window will result in a steeper slope (Figure 4A,
Higher Frequency), whereas a faster elongation rate will
tend to flatten the slope (Figure 4A, Faster Elongation).
Hence, by measuring the slopes and elongation rates for
each gene, the initiation frequencies can be estimated
(Figure 4A, B). Using this approach, we calculated the
relative initiation frequencies for all DRB-sensitive genes
(Additional file 6). To exclude the possibility that our calculation is biased by an excessively high signal in the proximity of the TSS, owing to Pol II pausing, relative initiation
frequencies were recalculated for all genes after excluding
the 2 first 2 Kb immediately downstream to the TSS. As
seen in Additional file 7, this did not affect significantly the
deduced initiation frequencies. Next, we compared the
4sUDRB-seq
Signal
A
initiation
|slope| =
elongation
position after TSS
4sUDRB-seq
Signal
Higher Frequency
Faster Elongation
initiation
initiation
|slope| =
|slope| =
elongation
elongation
position after TSS
position after TSS
B
4sUDRB-seq
signal
7
r = 0.4
6
5
4
3
60
K
13
K
12
13
40
12
13
12
13
00
K
20
K
8 min
Log2(Expression)
C
SRFBP1
12
To validate the rates deduced by 4sUDRB-seq, two
genes with different calculated elongation rates (RBM33
= 4.53 Kb/min, CLCN3 = 2 Kb/min) were examined at
higher temporal resolution, starting at 2 min after DRB
release. Pre-mRNA was quantified by qRT-PCR with
intron-derived primers. Reassuringly, the elongation rates
determined by qRT-PCR (RBM33 = 5 Kb/min, CLCN3 =
2.7 Kb/min) were in reasonably good agreement with the
rates determined by 4sUDRB-seq (Additional file 5A);
the small differences might be due to the lower spatial
resolution of qRT-PCR as compared to RNA-seq. Similar
results were observed by measuring the elongation
rates of those genes with different sets of primers
(Additional file 5B).
Histone post-translational modifications (PTMs) were
suggested to alter transcriptional elongation rates
[36-38]. Hence, we asked whether the variance in elongation rates correlates with differences in a particular
PTM. Interestingly 48% of genes in the upper quartile of
genes with the highest elongation rate were found to be
enriched in H3K79me2 within their gene body, compared to only 19% of genes from the slowest elongation
rate quartile (Fisher’s exact test, P = 5.5e-18; data not
shown). In contrast, no significant enrichment was
observed for H3K36me3 (Fisher’s exact test, P = 0.99).
Further analysis of genes that have H3K79me2 or
H3K36me3 in their gene body confirmed that
H3K79me2 is significantly higher in the fast elongation
rate quartile (t-test, P = 1e-7), while H3K36me3 is
relatively similar in both quartiles (t-test, P = 0.57)
(Figure 3D). Since the average expression level was similar in both quartiles (t-test, P = 0.43, data not shown),
the difference in H3K79me2 cannot simply be due to
differences in expression. Further experiments will be required in order to determine whether H3K79me2 accelerates elongation or the high levels of this PTM are
merely a consequence of the faster elongation. The fact
that we did not detect a correlation between H3K36me3
and elongation rates is surprising, since such correlation
was recently observed in response to a specific stimulus
[29]. This might suggest that elongation during induced
transcription utilizes a different set of histone PTMs than
those associated with constitutive basal transcription.
Page 6 of 10
Groups by
initiation frequency
Chr V
Calculation of genomewide relative transcription
initiation frequencies by 4sUDRB-seq
The 4sUDRB-seq reads are not uniformly distributed
throughout gene bodies, decreasing gradually towards the
3′ end of the gene (see Figures 2 and 3A). The slope of the
decreasing signal is dictated by the combined impact of the
transcription initiation frequency and the elongation rate
[39], “transcription initiation frequency” being defined here
as the average frequency at which Pol II molecules transition from an initiation mode into an elongation mode and
Figure 4 Calculation of genomewide relative transcription
initiation frequencies by 4sUDRB-seq. (A) Schematic representation
of the linear decrease in 4sUDRB-seq read intensity downstream to
the TSS of a hypothetical gene and in a gene with similar elongation
rate but higher initiation frequency (“Higher Frequency”) or in a
gene with similar initiation frequency but faster elongation rate
(“Faster Elongation”). (B) Analysis as in (A), exemplified for the 8 min
time point of the SRFBP1 gene, Red: linear fit of the signal upstream to
the boundary found by our algorithm. (C) Average expression levels of
four quartiles of genes binned by increasing initiation frequencies.
r indicates Pearson correlation. Error bars = standard errors.
Fuchs et al. Genome Biology 2014, 15:R69
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calculated relative initiation frequencies to gene expression
data in HeLa S3 cells (ENCODE). As seen in Figure 4C, a
positive correlation was found between initiation frequencies and expression levels. Since gene expression levels are
mostly determined by initiation frequencies [40], this correlation supports the reliability of the relative initiation
frequencies deduced by 4sUDRB-seq.
Conclusions
We report here that, by combining the reversible inhibitor
DRB and 4sU tagging, transcription elongation rates and
relative initiation frequencies within intact cells can be
relatively easily measured on a genomewide scale. This
method can be utilized in order to compare elongation
rates between different regions within genes, different tissues, or different biological contexts such as differentiation
and transformation. Moreover, by coupling 4sUDRB-seq
with depletion of specific transcription-regulatory factors,
one should be able to discriminate between factors that
impact gene expression by modulating transcription initiation frequencies and those that affect the elongation step.
Note: while this manuscript was under revision, an essentially similar method was described by Veloso et al. [41].
Materials and methods
Cell culture and treatments
Human cervical carcinoma HeLa cells were grown in
DMEM with 10% bovine serum supplemented with antibiotics and maintained at 37°C. 5,6-dichlorobenzimidazole 1-β-d-ribofuranoside (DRB) was purchased from
Sigma (D1916), and used at a final concentration of
100μM. 4-thiouridine (4sU) was purchased from Sigma
(T4509) and used at a final concentration of 1mM. In
two of the NT, 0 and 8 min samples, cells were transfected with non-targeting siRNA (Dharmacon) 48 h
before addition of DRB, employing the Dharmafect 1 reagent according to the manufacturer’s instructions.
RNA purification and quantitative RT-PCR
For quantitative reverse transcriptase PCR (qRT-PCR)
analysis, total RNA was extracted with the miRNeasy kit
(Qiagen). Two microgram of each RNA sample was reverse transcribed with Moloney murine leukemia virus
reverse transcriptase (Promega, Madison, WI, USA) and
random hexamer primers (Applied Biosystems). Realtime PCR was done in a StepOne real-time PCR instrument (Applied Biosystems, Foster City, CA, USA) with
Syber Green PCR supermix (Invitrogen).
Biotinylation and purification of 4sU labeled RNA
Biotinylation and purification of 4sU labeled RNA was
done as described in [42], with minor changes. A total of
100 to 180 μg RNA was used for the biotinylation reaction. 4sU-labeled RNA was biotinylated using EZ-Link
Page 7 of 10
Biotin-HPDP (Pierce), dissolved in dimethylformamide
(DMF) at a concentration of 1 mg/mL and stored at
-80°C. Biotinylation was done in labeling buffer (10 mM
Tris pH 7.4, 1 mM EDTA) and 0.2 mg/mL Biotin-HPDP
for 2 h with rotation at 25°C. Unbound Biotin-HPDP
was removed by chloroform/isoamylalcohol (24:1) extraction using MaXtract (high density) tubes (Qiagen).
RNA was precipitated at 20,000 g for 20 min at 4°C with
a 1:10 volume of 5M NaCl and an equal volume of isopropanol. The pellet was washed with an equal volume of
75% ethanol and precipitated again at 20,000 g for 10 min
at 4°C. The pellet was left to dry followed by resuspension
in 100 μL RNase-free water. Biotinylated RNA was captured using Dynabeads MyOne Streptavidin T1 beads
(Invitrogen). Biotinylated RNA was incubated with 100 μL
Dynabeads with rotation for 15 min at 25°C. Beads were
magnetically fixed and washed with 1× Dynabeads washing buffer. RNA-4sU was eluted with 100 μL of freshly
prepared 100 mM dithiothreitol (DTT), and cleaned on
RNeasy MinElute Spin columns (Qiagen).
Preparation and sequencing of 4sU labeled RNA
cDNA libraries were prepared with Illumina TruSeq
RNA sample preparation v2 kit according to the manufacturer’s instructions but without the polyA isolation
stage. Of note, strand specificity is not achieved with this
kit. Random hexamers were used for reverse transcription. Libraries were pooled and sequenced on an Illumina HiSeq 2500 system using the paired-end 50 mode.
Read mapping
All reads were aligned to the human reference genome
(hg19, GRCh37) using the bowtie 2 aligner with default
parameters (only setting -N 1) [43]. We considered only
reads mapped uniquely to the genome and not to
chrUn_gl000220 (rRNA) for further analysis. For each
experiment we constructed a genome-wide profile of the
signal per disjoint 100 bp adjacent bins. The sum of each
experiment was normalized to be 106.
Gene annotations
All gene annotations were taken from the hg19 RefGene
tab. of the UCSC browser.
Boundary detection algorithm
Only genes longer than 25 Kb and with at least 20 intron
bins with positive numerical values were included in
the boundary detection algorithm. In addition, in cases
where a gene has multiple variants, we consider only the
longest one.
‘Rough estimate’
For each of the filtered genes from the different 4 min
or 8 min samples we extracted only the profile data of
Fuchs et al. Genome Biology 2014, 15:R69
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introns, in order to avoid the confounding effects of
reads derived from contaminating mature mRNA. In
order to increase the reliability of the signal used for
normalization, and to reduce the effects of variability between the 0 min samples due to low read numbers, the
profile of the 0 time point was created by averaging four
different biological repeats of the 0 min samples. Next,
the intron data were smoothed using cubic spline
(smoothing parameter - 10-5).
To normalize each gene in each experiment (0, 4, and
8 min) by the background signal, we performed the following procedure. First, for each bin of the gene we estimated
the background signal downstream to this bin up to the 3’
end of the gene. This background read distribution was
built out of a negative binomial signal. To estimate the
average of the background distribution we took the most
abundant value. The most abundant value was identified
by building a histogram of the data and selecting the bin
with the highest count. In cases where we had fewer than
10 values downstream to the last point with positive signals
we used the background estimate of the upstream bin.
We next compared the 4 and 8 min normalized profiles
to the 0 time point. For each of the 4 and 8 min samples,
we chose the first bin downstream the TSS + 2.5 Kb
(defined arbitrarily) where the sample’s signal is not higher
than the 0 time point at the same position (up to 10%).
This bin was defined as the ‘rough estimate’ of the boundary. If the first position (TSS + 2.5 Kb) was identified or
no position was identified no boundary was determined.
Refinement using profile derivative
For each gene we took the profile of the introns and as
in the ‘rough estimate’, we used cubic spline with the
same parameter. Before smoothing the 0 time point bins,
we added the value 0.01 to each bin (according to the
‘Rule of Succession’) in order to prevent cases where
data are normalized to 0 time points and results in dividing by 0. Then we divided the 4 and 8 min gene profiles by the 0 time point profile and obtained the
derivative by subtracting each adjacent bin.
We looked for the local minimums of the derivative
using the N.Yoder peak finder (mathworks.com) upstream the ‘rough estimate’ boundary; we considered
only derivatives smaller than -0.01. We took the most
downstream minimum relative to the ‘rough estimate’
boundary. Next, we identified the most downstream bin
relative to the minimum (up to 2 Kb downstream to the
‘rough estimate’) where the derivative was close to 0
(> = -0.002) and defined it as the ‘refined estimate’. If no
such bin was found we adhered to the ‘rough’ estimate.
Page 8 of 10
the algorithm in both of the 4 min samples and in at least
three of the four 8 min samples were included. Next, genes
in which the average of the 8 min boundaries was lower
than that of the 4 min boundaries were excluded. Since we
had four biological repeats for the 8 min samples, we used
modified Thompson tau outlier method in order to eliminate a maximum of one outlier. Next, the elongation rate
was calculated by linearly fitting the averaged 4 and 8 min
boundaries as a function of time. The slope of the linear fit
was defined as the elongation rate. Next, the 50% confidence interval of the slope was defined as the confidence
interval of the elongation rate. Only genes with confidence
intervals better than 0.5 Kb/min were retained. The slope
was also used to define the delay time, which was defined
as being equal to the slope intersection with the time axis.
The elongation rate was determined only for genes with
delay times between -1 and 4 min.
Processing of ENCODE data
H3K36me3/H3K79me2 data of Hela-S3 were derived
from the ENCODE project [44]. Peak calling for
H3K36me3 and H3K79me2 was done as follows: for
each gene the 20 Kb downstream to the TSS were
binned into 100 bp bins, and peak calling was done
using N.Yoder peak finder (mathworks.com). Only genes
with at least one peak were selected. Next, in order to
get an enrichment level for each gene we averaged for
each modification the signal for all bins.
RNA-seq data of Hela-S3 were also from the ENCODE
project (Caltech RnaSeq Helas3 200SigRep1V4) [44]. Expression per gene was the average for the signal on all exons.
Calculating relative initiation frequency
For a constant initiation frequency I and a constant
RNA polymerase velocity V, the relative amount of
polymerases that have moved downstream to location x
in the gene within T minutes (T = 8 in our case) from
DRB removal will be (with delay time D):
x
I
SignalðxÞ ¼ I T −D−
¼ I ðT−DÞ−x
V
V
To infer the initiation frequency we calculated the best
linear fit of the intron signal as a function of Kb downstream to the TSS: ax + b. If we denote by B the 8
minutes boundary, we get:
aB þ b≈0ðNot exactly zero due to noiseÞ
Adding the previous equality we get:
I≈
Calculating elongation rate and rate confidence interval
In order to calculate the elongation rates, only genes with
for which clear elongation boundaries were identified by
bV
B
The intron signal was derived from each 8 min sample,
divided by the averaged signal from the 0 min samples
Fuchs et al. Genome Biology 2014, 15:R69
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Page 9 of 10
plus 1 (as pseudocounts to prevent dividing by 0). This
calculation was performed separately for all four biological repeats and outliers were excluded using the
modified Thompson Tau outlier method. Relative initiation frequencies were determined by averaging the rates
from the biological repeats.
Primers
Primers used in this study: OPA1 mRNA F 5′-TT
TTTACCTCAGGTTCTCCGGA, OPA1 mRNA R 5′CA
CGATCTGTTGCTCTAAACGC; OPA1+2 Kb F 5′ ACC
ATGGATGCCATTGAGTCA, OPA1+2 Kb R 5′TGTGC
CATCACCAGGAGACAT; OPA1+10 Kb F 5′TCTGTT
CCATGATGAGCTGTGG, OPA1+10 Kb R 5′CCTGGT
CCTTCCTGAATCTTTG; TTC17 mRNA F 5′ATCAA
AGCCAAGGTGCCCT, TTC17 mRNA R 5′GGACTGA
TGTCTTTGCTCTCCA; TTC17+1 Kb F 5′ TCAGAGG
CGAGACTGCTTTTC, TTC17+1 Kb R 5′GCATTTAC
AAAACGCAGGCA; TTC17+10 Kb F 5′ TCCAGCCTC
AGACACCACTTT, TTC17+10 Kb R 5′ TGGTTTGA
AGAACATCCCGAG; RBM33+5 Kb F 5′TTCCACATC
TTCCTGGCACA, RBM33+5 Kb R 5′ACACAGGTGA
ATCATGTGGAAT; RBM33+10 Kb F 5′GAACTCAGC
CTCTGTGCTGT, RBM33+10 Kb R 5′ACAATGTGA
TGAGGGCTGGG; RBM33+14.2 Kb F 5′TCCTCTCTG
CCAGTCTGTGA, RBM33+14.2 Kb R 5′GAAGGACT
GGCATCTGGCAT; RBM33+20 Kb F 5′CCATGATT
TGGAAAAGTGTGACGA, RBM33+20 Kb R 5′TGTGA
CATGCTAAAAAGTTAGAGAC; CLCN3+4 Kb F 5′
ACAGCATCCCTCTTGAGGAAAA, CLCN3+4 Kb R 5′
GGCCCTACAGCTTTCAGTAGA; CLCN3+9.3 Kb F 5′
ACGTTCTACAATGGCAGACAGA, CLCN3+9.3 Kb R
5′GCTTCGCTGACCCACTTACT; CLCN3+20 Kb F 5′
ACAGGAAGGGAGAGCCAAGA, CLCN3+20 Kb R 5′
AGCTGCACTCATGAACAGTCA; 18S F 5′CGCCGCT
AGAGGTGAAATTCT, 18S R 5′CATTCTTGGCAAAT
GCTTTCG.
Data availability
Raw data have been deposited into GEO with accession
number: GSE57116.
Additional files
Additional file 1: Related to Figure 1: Schematic representation of
biological samples for 4sUDRB-seq. HeLa cells were either not treated
(NT) or treated with DRB for 3 hours and harvested either 4 or 8 min
following DRB removal. In both cases, the cells were pulsed with 4sU
for the last 8 min before being harvested.
Additional file 2: Related to Figure 2: Correlation between
biological repeats. Pearson correlation between the average signals
over introns for all genes longer than 10Kb. The specific treatment
(NT, 0 min, 4 min, 8 min) and the sample number (1,2,3,4) are indicated
for each repeat sample.
Additional file 3: Related to Figure 3: Confidence interval
distribution. Confidence interval distribution for all genes for which
elongation boundaries were successfully detected by our algorithm in
the 4 and 8 min samples. Genes left to the red dotted line were taken
for further analysis as described in the text.
Additional file 4: Related to Figure 4: List of elongation rates and
their confidence intervals. Only genes with confidence intervals better
than ±0.5 Kb were included.
Additional file 5: Related to Figure 5: Validation of transcription
elongation rates. (A, B) qRT-PCR analysis of RBM33 and CLCN3 premRNA in HeLa cells, without DRB treatment (NT) and at the indicated
time points after DRB removal. To simulate the experimental conditions
of the DRB-seq experiment, 4sU was added to all cultures 8 min before
harvesting, although subsequent biotinylation and purification were not
performed because the use of intronic primers in the qRT-PCR procedure
already selects for pre-mRNA. All values were normalized to 18S RNA in
the same sample. Bars indicate averages of data from duplicate qPCR
reactions; error bars represent standard deviation. (A) and (B) are derived
from two independent experiments, using different qPCR primer pairs.
Additional file 6: Related to Figure 6: List of relative transcription
initiation frequencies. Relative initiation frequencies were calculated by
averaging slope values of four biological repeats of the 8 min samples.
Outliers were excluded.
Additional file 7: Related to Figure 7: Correlation of relative
initiation frequencies calculated either with or without inclusion
of the first 2Kb of genes. Scatter plot of relative initiation frequencies
calculated for full transcripts (x-axis) vs. relative initiation frequencies
calculated after excluding the first 2 Kb of each transcript (y-axis).
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
GF, YV, IA and MO conceived the study and designed the experiments. GF
performed the experiments. YV analyzed the data. SB and SG generated the
libraries for sequencing and performed the sequencing. GF, YV and MO
wrote the paper. All authors have read and approved the manuscript for
publication.
Acknowledgements
We thank Michal Rabani, Eitan Yaffe, Ilya Soifer and Dror Hollander for
helpful discussions and Minho Chae and W. Lee Kraus for sharing with us
the implementation of the Gro-HMM. This work was supported in part by a
student-initiated project grant from the Kahn Center for Systems Biology,
grant 293438 (RUBICAN) from the European Research Council, the Dr. Miriam
and Sheldon G. Adelson Medical Research Foundation, and the Lower
Saxony-Israeli Association. MO is incumbent of the Andre Lwoff chair in
molecular biology.
Author details
Department of Molecular Cell Biology, Weizmann Institute of Science,
Rehovot 76100, Israel. 2Department of Molecular Genetics, Weizmann
Institute of Science, Rehovot 76100, Israel. 3The Israel National Center for
Personalized Medicine (INCPM), Weizmann Institute of Science, Rehovot
76100, Israel. 4Department of Immunology, Weizmann Institute of Science,
Rehovot 76100, Israel.
1
Received: 10 February 2014 Accepted: 9 May 2014
Published: 9 May 2014
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Cite this article as: Fuchs et al.: 4sUDRB-seq: measuring genomewide
transcriptional elongation rates and initiation frequencies within cells.
Genome Biology 2014 15:R69.